class_docstr: Add random camera radial distortion.
  
  .. image:: _static/img/RandomFisheye.png
  
  Args:
      center_x: Ranges to sample respect to x-coordinate center with shape (2,).
      center_y: Ranges to sample respect to y-coordinate center with shape (2,).
      gamma: Ranges to sample for the gamma values respect to optical center with shape (2,).
      same_on_batch: apply the same transformation across the batch.
      p: probability of applying the transformation.
      keepdim: whether to keep the output shape the same as input (True) or broadcast it
               to the batch form (False).
  
  Examples:
      >>> import torch
      >>> img = torch.ones(1, 1, 2, 2)
      >>> center_x = torch.tensor([-.3, .3])
      >>> center_y = torch.tensor([-.3, .3])
      >>> gamma = torch.tensor([.9, 1.])
      >>> out = RandomFisheye(center_x, center_y, gamma)(img)
      >>> out.shape
      torch.Size([1, 1, 2, 2])
  
  To apply the exact augmenation again, you may take the advantage of the previous parameter state:
      >>> input = torch.randn(1, 3, 32, 32)
      >>> aug = RandomFisheye(center_x, center_y, gamma, p=1.)
      >>> (aug(input) == aug(input, params=aug._params)).all()
      tensor(True)
code: def apply_transform(
      self,
      input: Tensor,
      params: Dict[str, Tensor],
      flags: Dict[str, Any],
      transform: Optional[Tensor] = None,
  ) -> Tensor:
      # create the initial sampling fields
      B, _, H, W = input.shape
      grid = create_meshgrid(H, W, normalized_coordinates=True)
      field_x = grid[..., 0].to(input)  # 1xHxW
      field_y = grid[..., 1].to(input)  # 1xHxW
      # vectorize the random parameters
      center_x = params["center_x"].view(B, 1, 1).to(input)
      center_y = params["center_y"].view(B, 1, 1).to(input)
      gamma = params["gamma"].view(B, 1, 1).to(input)
      # compute and apply the distances respect to the camera optical center
      distance = (
          (center_x - field_x) ** 2 + (center_y - field_y) ** 2
      ) ** 0.5
      field_x = field_x + field_x * distance**gamma  # BxHxw
      field_y = field_y + field_y * distance**gamma  # BxHxW
      return remap(
          input,
          field_x,
          field_y,
          normalized_coordinates=True,
          align_corners=True,
      )
  
file_path: kornia/kornia/augmentation/_2d/geometric/fisheye.py
imports: from typing import Any, Dict, Optional
  from kornia.augmentation import random_generator
  from kornia.augmentation._2d.base import AugmentationBase2D
  from kornia.core import Tensor
  from kornia.geometry.transform import remap
  from kornia.utils import create_meshgrid
label_desc: Use image node_id 3 for calling the RandomFisheye obj's underlying member method code with example usage: obj.apply_transform(input, params, flags, transform) and returns: remap